Indicators used in the data collection phase.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller’s cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.停机坪管制员（ramp controller）需在处置复杂运行任务的同时有效管控自身工作负荷，这是提升航空安全的关键环节。精准感知管制员瞬时工作负荷的能力，对保障运行效能、防范危险事故至关重要。 本文提出一种全新方法，旨在通过融合并优化眼动、呼吸及疲劳特征的特征组合，提升对停机坪管制员累积工作负荷的评估效果。 具体而言，本研究依托上海虹桥机场的真实运行数据，开展了一项时长90分钟的停机坪管制模拟实验，共收集8名管制员的多类型数据。在完成数据构建与多类型特征提取后，本研究通过无监督学习对所有样本的工作负荷进行分类。随后，在完成数据对齐后，本研究采用监督学习方法计算特征权重并训练分类器。通过计算特征权重确定最优特征组合，最终由K近邻（KNN）分类器实现了超过98%的分类准确率。 此外，本研究还针对不同工作负荷等级开展了数值评估与阈值计算，并对结果进行了解释说明。本研究可为未来以人为中心的数据构建、处理与解读相关工作提供思路，助力航空领域工作负荷评估技术的进步。